Connected Semantic Concepts as a Base for Optimal Recording and Computer-Based Modelling of Cultural Heritage Objects

Ponciano, Jean-Jacques; Karmacharya, Ashish; Wefers, Stefanie; Atorf, P.; Boochs, Frank

doi:10.1007/978-3-319-99441-3_31

Cited by 4 publications

(6 citation statements)

References 9 publications

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“…Thus, the strategy to recognize the watermills consists of first recognizing the different rooms present in the application case. The detection of rooms is explained in [45] and consists of identifying the floors connected to at least two walls to form a room. The detection of walls and floors is explained in [41], and consists of applying a segmentation mainly based on point orientation, then extracting the segments' geometrical characteristics to classify them according to these characteristics.…”

Section: Results Of Watermill Recognition In Ephesos Terrace Housementioning

confidence: 99%

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From Acquisition to Presentation—The Potential of Semantics to Support the Safeguard of Cultural Heritage

Ponciano¹,

Prudhomme²,

Boochs³

2021

Remote Sensing

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The signature of the 2019 Declaration of Cooperation on advancing the digitization of cultural heritage in Europe shows the important role that the 3D digitization process plays in the safeguard and sustainability of cultural heritage. The digitization also aims at sharing and presenting cultural heritage. However, the processing steps of data acquisition to its presentation requires an interdisciplinary collaboration, where understanding and collaborative work is difficult due to the presence of different expert knowledge involved. This study proposes an end-to-end method from the cultural data acquisition to its presentation thanks to explicit semantics representing the different fields of expert knowledge intervening in this process. This method is composed of three knowledge-based processing steps: (i) a recommendation process of acquisition technology to support cultural data acquisition; (ii) an object recognition process to structure the unstructured acquired data; and (iii) an enrichment process based on Linked Open Data to document cultural objects with further information, such as geospatial, cultural, and historical information. The proposed method was applied in two case studies concerning the watermills of Ephesos terrace house 2 and the first Sacro Monte chapel in Varallo. These application cases show the proposed method’s ability to recognize and document digitized cultural objects in different contexts thanks to the semantics.

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Section: Results Of Watermill Recognition In Ephesos Terrace Housementioning

confidence: 99%

“…A floor is under the walls and can be connected with another floor. The complete object modeling is detailed in [45].…”

Section: Watermill Of the Ephesos Terrace Housementioning

confidence: 99%

From Acquisition to Presentation—The Potential of Semantics to Support the Safeguard of Cultural Heritage

Ponciano¹,

Prudhomme²,

Boochs³

2021

Remote Sensing

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“…These two components communicate together through SPARQL queries (Prud'hommeaux and Seaborne 2008) in order to (a) allow the knowledge to guide data processing by executing an algorithm, which has been selected and defined as executable and (b) allow the algorithm part to enrich the knowledge through a representation of the algorithm execution result. This knowledge-based approach of data processing has shown its performance and ability of adaptation to different contexts of application in previous works (Ponciano et al 2019a), whose building indoor context (Ponciano et al 2019b). It processes the point cloud data iteratively through three main steps: segmentation, classification, and a self-learning process (Ponciano 2019).…”

Section: Knowledge-based Data Processingmentioning

confidence: 93%

Bim on Artificial Intelligence for Decision Support in E-Health

Plaß

Prudhomme

Ponciano

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Looking ahead of 2070, the number of the elderly population will increase rapidly in the European Union and beyond. As society ages, it will be confronted to novel challenges related with other concerns like the concept aging in place that the majority of the elderly prefer. Concerning that, the living space must be adapted to the requirements of people with a disability, to support their relatives or friends that will become more and more important in future due to a lack of professional’s and both overstressed and expensive hospitals or nursing homes. Compounding this, those living space requirements are highly individual, depending on the disease. Our study focuses on a medical white box decision support system providing advice even for unknowledgeable users by evaluating the suitability of an elderly’s living environment in terms of their individual disease. In this paper, we propose tackling this issue with a decision support system linked to Building Information Modeling (BIM) and based on Artificial Intelligence using semantic technologies. The proposed approach's contribution is a reliable process that uses up-to-date 3D point cloud data of the person’s living environment and predicts suitable, non-suitable and adaptable zones therein according to different pathologies using formalised knowledge. We are able to provide deep expert knowledge linked from different domains inside a knowledge base and thus produce an outcome through BIM, which is understandable and helpful for two types of users, ordinary people concerned by the matter and building experts. We illustrate our methodology by a proof of concept concerning a wheel-chaired person.

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“…This semantic-based methodology of classification is based on a classical segmentation processing that limits the performance of the semantic classification if the segmentation process is not adapted to the object to detect and its representation in the knowledge base. Therefore some approaches, such as References [38,39], combine the segmentation and classification process simultaneously through ontology-driven approaches. The use of explicit knowledge about data, object and algorithm in the segmentation process aims at providing adaptability in the selection of the algorithm to segment the data and thus allows an automatic adaptation of the segmentation process.…”

Section: Related Workmentioning

confidence: 99%

“…The presented approach adapts algorithms for every combination of specific objects and data. This approach improves the previous approach [39] firstly by adding knowledge about data acquisition to deduce information about data characteristics and object representation inside the data. This knowledge aims at supporting the adaptation of the detection process to the variations of data characteristic and object representation inside the same data.…”

Section: Contributionsmentioning

confidence: 99%

Automatic Detection of Objects in 3D Point Clouds Based on Exclusively Semantic Guided Processes

Ponciano

Trémeau

Boochs

2019

IJGI

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In the domain of computer vision, object recognition aims at detecting and classifying objects in data sets. Model-driven approaches are typically constrained through their focus on either a specific type of data, a context (indoor, outdoor) or a set of objects. Machine learning-based approaches are more flexible but also constrained as they need annotated data sets to train the learning process. That leads to problems when this data is not available through the specialty of the application field, like archaeology, for example. In order to overcome such constraints, we present a fully semantic-guided approach. The role of semantics is to express all relevant knowledge of the representation of the objects inside the data sets and of the algorithms which address this representation. In addition, the approach contains a learning stage since it adapts the processing according to the diversity of the objects and data characteristics. The semantic is expressed via an ontological model and uses standard web technology like SPARQL queries, providing great flexibility. The ontological model describes the object, the data and the algorithms. It allows the selection and execution of algorithms adapted to the data and objects dynamically. Similarly, processing results are dynamically classified and allow for enriching the ontological model using SPARQL construct queries. The semantic formulated through SPARQL also acts as a bridge between the knowledge contained within the ontological model and the processing branch, which executes algorithms. It provides the capability to adapt the sequence of algorithms to an individual state of the processing chain and makes the solution robust and flexible. The comparison of this approach with others on the same use case shows the efficiency and improvement this approach brings.

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Connected Semantic Concepts as a Base for Optimal Recording and Computer-Based Modelling of Cultural Heritage Objects

Cited by 4 publications

References 9 publications

From Acquisition to Presentation—The Potential of Semantics to Support the Safeguard of Cultural Heritage

From Acquisition to Presentation—The Potential of Semantics to Support the Safeguard of Cultural Heritage

Bim on Artificial Intelligence for Decision Support in E-Health

Automatic Detection of Objects in 3D Point Clouds Based on Exclusively Semantic Guided Processes

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